python-pour-finance/04-Visualisation-Matplotlib-Pandas/04-01-Matplotlib/.ipynb_checkpoints/Matplotlib-checkpoint.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___\n",
    "\n",
    "<a href='http://www.pieriandata.com'> <img src='../../Pierian_Data_Logo.png' /></a>\n",
    "___\n",
    "# Matplotlib Overview Lecture"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Matplotlib is the \"grandfather\" library of data visualization with Python. It was created by John Hunter. He created it to try to replicate MatLab's (another programming language) plotting capabilities in Python. So if you happen to be familiar with matlab, matplotlib will feel natural to you.\n",
    "\n",
    "It is an excellent 2D and 3D graphics library for generating scientific figures. \n",
    "\n",
    "Some of the major Pros of Matplotlib are:\n",
    "\n",
    "* Generally easy to get started for simple plots\n",
    "* Support for custom labels and texts\n",
    "* Great control of every element in a figure\n",
    "* High-quality output in many formats\n",
    "* Very customizable in general\n",
    "\n",
    "Matplotlib allows you to create reproducible figures programmatically. Let's learn how to use it! Before continuing this lecture, I encourage you just to explore the official Matplotlib web page: http://matplotlib.org/\n",
    "\n",
    "## Installation \n",
    "\n",
    "You'll need to install matplotlib first with either:\n",
    "\n",
    "    conda install matplotlib\n",
    "or\n",
    "    pip install matplotlib\n",
    "    \n",
    "## Importing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import the `matplotlib.pyplot` module under the name `plt` (the tidy way):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You'll also need to use this line to see plots in the notebook:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That line is only for jupyter notebooks, if you are using another editor, you'll use: **plt.show()** at the end of all your plotting commands to have the figure pop up in another window."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Basic Example\n",
    "\n",
    "Let's walk through a very simple example using two numpy arrays:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Example\n",
    "\n",
    "Let's walk through a very simple example using two numpy arrays. You can also use lists, but most likely you'll be passing numpy arrays or pandas columns (which essentially also behave like arrays).\n",
    "\n",
    "** The data we want to plot:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "x = np.linspace(0, 5, 11)\n",
    "y = x ** 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0. ,  0.5,  1. ,  1.5,  2. ,  2.5,  3. ,  3.5,  4. ,  4.5,  5. ])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  0.  ,   0.25,   1.  ,   2.25,   4.  ,   6.25,   9.  ,  12.25,\n",
       "        16.  ,  20.25,  25.  ])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Basic Matplotlib Commands\n",
    "\n",
    "We can create a very simple line plot using the following ( I encourage you to pause and use Shift+Tab along the way to check out the document strings for the functions we are using)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAEWCAYAAABrDZDcAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b3f677f2e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, y, 'r') # 'r' is the color red\n",
    "plt.xlabel('X Axis Title Here')\n",
    "plt.ylabel('Y Axis Title Here')\n",
    "plt.title('String Title Here')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating Multiplots on Same Canvas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD8CAYAAABn919SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3Xuc1HP///HHa2u1HeksFYUcEkpdORQq6lIqkXTiSiLh\nR8TX8XK+SC4XXUhXiygqSflWpONVKrQd6EsHiZTKdj6nNtu+f398NpIO2+7MvGc+87zfbnubndnZ\nmefoMy/vfc/7836Zcw4REUl8Kb4DiIhIZKigi4iEhAq6iEhIqKCLiISECrqISEiooIuIhIQKuohI\nSKigi4iEhAq6iEhIFI7lk5UrV85Vq1Ytlk8pSWTevHkbnHPlfTy3jm2Jprwe2zEt6NWqVWPu3Lmx\nfEpJIma2wtdz69iWaMrrsa0pFxGRkFBBFxEJCRV0EZGQUEEXEQkJFXQRkZA4YkE3s6pmNtXMFpnZ\nQjPrmXv7E2a22szm5361iH5ckdgws+Vm9k3usa3lKxJVmdszufTtS1mzY02BHicvI/Rs4F7nXE3g\nAuAOM6uZ+7OXnHO1c7/GFSiJSPxpnHts1/MdRMLt6elPM/OnmTz16VMFepwjrkN3zmUCmbnfbzez\nxUDlAj2riIhQ9Jmi7M7e/dv1/nP7039uf9IKp7HrkV1H/XhHNYduZtWAOkBG7k13mtnXZjbQzEof\n4ne6m9lcM5u7fv36ow4o8pt774UBA2L1bA6YbGbzzKz7we6gY1sKal73eVQu+fv4uFjhYnQ+uzM/\n9vwxX4+X54JuZiWAkcDdzrltQH/gZKA2wQj+Xwf7PedcunOunnOuXvnyXs7KljDYsAFeeQW++y5W\nz9jQOVcbaE4wzXjJgXfQsS0FkbEqgxZDWvDz9p8xjLTCaezeu5tSRUpxfInj8/WYeSroZpZKUMyH\nOOdGATjn1jrn9jrncoDXgfr5SiCSF8OHw6+/wt/+FpOnc86tzr1cB3yIjm+JEOccfWf15eK3LsbM\nuPSkS7mt3m3M6jaLHnV7FOiD0SPOoZuZAW8Ci51zL+53e6Xc+XWAq4EF+U4hciSDBsG55wZfUWZm\nxYGU3M+MigPNgIJ9WiUCbN61ma6juzJ6yWjanNGGga0HUrro77PV/a7sV6DHz8vmXA2AG4BvzGx+\n7m0PAx3NrDbBXONy4NYCJRE5lMWLYc4c+NdBZ/WioSLwYTCWoTAw1Dk3PlZPLuE0e/Vs2n/QnlXb\nVvHSX1+i5/k9yT3GIiYvq1xmAgd7Vi1TlNjIyoIWLaBTp5g8nXNuGRD9PwUkKTjneDnjZf5n0v9w\nQskTmNl1JudXOT8qzxXT7XNF8qV2bfj4Y98pRI7alt1buGn0TXz47Ye0Pr01b131FmWKlona86mg\nS3z78UdITYUqVXwnETkqc3+ey3UjrmPltpX8q9m/uOeCeyI+xXIg7eUi8e2JJ+Ccc4IVLiIJwDnH\nKxmvcNGbF7HX7WVG1xn0urBX1Is5aIQu8WzHDhg5Mpg7T031nUbkiLbu3kq3Md0YuXgkLU9ryaA2\ng6I6xXIgFXSJXx9+CDt3xmztuUhBzPt5Htd9cB0rtqzgn03/Sa8Le5FisZ0EUUGX+DVoEJx8MjRo\n4DuJyCE553htzmv0mtiLCsUrML3rdC6qepGXLCroEp82boTp0+GRRyAGc48i+bF191ZuGXsLIxaN\noEWNFgxuM5iyxcp6y6OCLvGpbFlYsUJz5xK3vsz8kutGXMfyLcvpc3kf7rvovphPsRxIBV3iV6VK\nvhOI/Ilzjv/M/Q93T7ib8sXKM+3GaTQ8saHvWICWLUo8mjsXLr8cli71nUTkD7ZlbaPjyI7cPu52\nmlRvwvwe8+OmmING6BKPBg2CmTOhQgXfSUR+M3/NfNqNaMePm3+k92W9ub/B/d6nWA6kgi7xZc8e\nGDYM2rSBY4/1nUaSXOb2TDp80IEra1zJY9Meo2yxskztMpWLT7rYd7SDUkGX+DJuXLDCRWvPJQ48\nOvVRpv80nek/TafZKc145+p3qFA8fv9yVEGX+DJ4MFSsCM2a+U4iSezAXp8AE3+YyEl9T8pXr89Y\nia8JIJHGjeGhh6Cwxhriz8DWA0lN+X3JbEF7fcaK3jUSX+6803cCSXL9Zvej5/ieHFvkWDbv3kyR\nwkUK3OszVlTQJX6MHRuM0EuU8J1EklB2Tjb3jL+HV+e8ypU1riTFUqhaqird63YnfV46mTsyj/wg\nnqmgS3z49lto3TpoM9erl+80kmS27t5K+w/aM+GHCfS6oBfPN32eQimFfvt5QXt9xooKusSHd96B\nQoVi1mZOZJ9lm5fRcmhLlm5ayuutXufm8272HSnfVNDFv5ycoKD/9a9wfHzPUUq4zFgxg6uHX02O\ny2Hi9RNpXL2x70gFolUu4t+0abBypdaeS0y9Pf9tLht8GWWLlSXj5oyEL+aggi7xYMqU4KzQ1q19\nJ5EkkONyeHDyg3Qd3ZVLTrqEWd1mUaNsDd+xIkIFXfx75hlYtAiKFvWdREJu556dtH2/LX0+68Ot\ndW/lk86fULpoad+xIkZz6OKXc0EDixNO8J1EQm7VtlW0GtaKr9d+Td+/9uWu8++KSePmWFJBF7+u\nvhpq1oRnn/WdREJszuo5tH6vNTv37OSjjh/RvEZz35GiQlMu4s/KlTBmDBxzjO8kEmLvL3yfS96+\nhLTCaXze7fPQFnNQQRefhgwJplxuuMF3Egkh5xxPf/o07T9oT91Kdcm4OYNaFWr5jhVVmnIRP5wL\nGlk0bAinnOI7jYTM7uzddBvTjaHfDOWGc27g9VavU6RwEd+xok4FXfyYOzc43T893XcSCZm1O9bS\nZngbZq2axTNNnuGhhg+F7sPPQ1FBFz/Kl4d774V27XwnkRD5Zu03tBzWkvU71/NBuw9oW7Ot70gx\ndcQ5dDOramZTzWyRmS00s565t5cxs0lmtjT3MjyLOSX6qlWDF16A447zneSQzKyQmX1lZh/5ziJH\n9tF3H3HRwIvIzslmRtcZSVfMIW8fimYD9zrnagIXAHeYWU3gQWCKc64GMCX3usiRzZkTnB2ak+M7\nyZH0BBb7DiGHlrk9k0vfupQnpj1B62GtOb3s6cy+eTZ1T6jrO5oXR5xycc5lApm53283s8VAZeAq\noFHu3QYB04AHopJSwuXvf4eFC2H5ckiJz4VWZlYFuBJ4BtB+vnHqiU+f+K3nZ9sz2zL46sEUSy3m\nO5Y3RzWHbmbVgDpABlAxt9gDrAEqRjSZhNPSpTBxIjz5ZLy3mesL3A+U9B1E/uxgPT9HLh7Jx0s/\njuuen9GW5+GRmZUARgJ3O+e27f8z55wD3CF+r7uZzTWzuevXry9QWAmBAQOCfc9vjt89p82sJbDO\nOTfvCPfTse3J/FvnU65Yud+uJ0rPz2jLU0E3s1SCYj7EOTcq9+a1ZlYp9+eVgHUH+13nXLpzrp5z\nrl758uUjkVkS1a5dMHBgcLp/fO/d0gBobWbLgfeAJmb27oF30rHtx4ZfNtB5VGc2/rIRw0grnJYw\nPT+jLS+rXAx4E1jsnHtxvx+NAbrkft8FGB35eBIqS5ZAkSJw222+kxyWc+4h51wV51w1oAPwX+fc\n9Z5jCcGHoI3ebsSCdQu4oMoF3FbvNmZ1m0WPuj1Ys2ON73je5WUSswFwA/CNmc3Pve1h4DngfTPr\nBqwArotORAmN2rVhxQpITfWdRBLQii0ruPydy8ncnsknnT/5Q0OKROn5GW15WeUyEzjUaVaXRTaO\nhNaWLVCiRMJtxOWcm0awgks8WrpxKZcNvoxtWduYdMMkLqx6oe9IcSk+14xJ+Nx/P9SqBXv3+k4i\nCWbBugVc8vYl7MrexdQuU1XMD0MFXaJv69ZgZ8WGDYMVLiJ5NO/neVz69qUYxvQbp1OnUh3fkeKa\nCrpE3+DB8Msvcf9hqMSXmT/NpMngJpQ8piQzus7gzPJn+o4U91TQJbqcg/794S9/gbrJeTq2HL3J\nyybz13f/yvEljmdG1xmcUkZbLOeFCrpE18yZsHgx3H677ySSIMYuGcuVQ6/klNKnMP3G6VQ9tqrv\nSAkjrs+9lhBo0ADGjYNGjXwnkQQwfMFwrv/weuocX4fx14+nTNEyviMlFI3QJbpSUqB5cyha1HcS\niXMDvxpIx5EduajqRUz+22QV83xQQZfoefVVePDBRNgmVzx7OeNluo3pRrNTmvFJ508oVaSU70gJ\nSQVdoiM7G55/Hr78Mm63yJX40HtGb3qO70mbM9owusPopN7+tqD0TpPo+PhjWLlSSxXlkJxzPDLl\nER7+78N0OrsT71/7flI0co4mfSgq0dG/P1SuDK1a+U4icSjH5XDP+Ht4efbL3HLeLfS/sj+FUnTS\nWUFphC6R9/33MGECdO8e700sxIO9OXu5ZcwtvDz7Ze4+/24GtBygYh4herdJdHTqFNdNLCT2Mrdn\n0v6D9pQpWobRS0bz6CWP8mSjJwl26JZIUEGXyDv11GDvFpH9PD7tcWb8NAOAPpf34f4G93tOFD4q\n6BJZs2YF2+TWquU7icSJg/X/fGDyAzw+7fGk7v8ZDZpDl8i6+25o1y7Yw0UE+OHOHzj5uJN/u67+\nn9Gjgi6R89VXkJEBPXqA5kUl16tzXmXZlmXq/xkDmnKRyOnfPzjFv0uXI99XksK/Z/2b3jN7U+3Y\najSv0Zxb695K+rx0Mndk+o4WSiroEhn7mlh06gTHHec7jcSBYd8M4+4Jd3P1GVczot2I35Ymqv9n\n9GjKRSJj3rxgmkXb5Aow8YeJdPnfLlx60qUMbTtU68xjRCN0iYwmTWDNmmCFiyS12atnc83wa6hZ\nviajO4wmrXCa70hJQyN0KbidO4NVLSrmSW/JhiW0GNKCCsUr8EnnTzg27VjfkZKKCroUXNeucPnl\nWqqY5FZvW02zd5tRKKUQE2+YSKWSlXxHSjoq6FIwmZnw4YdQu7aWKiaxzbs2c8WQK9i0axOfdP6E\nU8uc6jtSUtIcuhTMm28Ge5/36OE7iXjyy6+/0GpYK77b+B3jOo3jvErn+Y6UtFTQJf/27oX0dGja\nFGrU8J1GPMjOyab9B+35fOXnDL92OJedfJnvSElNUy6Sf2PGqIlFEnPO0X1sdz767iNebfEq7c5q\n5ztS0lNBl/xr0QLefRdat/adRDx4eMrDvDX/LR675DFu/4vOP4gHmnKR/CtSBDp39p1CPOg7qy/P\nffYct9a9lScaPeE7juTSCF3y56abYNAg3ynEgyFfD+GeCffQ9sy29GvRTw0q4ogKuhy9efPgrbdg\n7VrfSSTGxn8/nhtH30ijao1495p3dUp/nDliQTezgWa2zswW7HfbE2a22szm5361iG5MiSt9+sCx\nx4Z2qaKZpZnZbDP7PzNbaGZP+s7kU+b2TC59+1LGfTeOtu+3pVaFWjqlP07lZQ79beBVYPABt7/k\nnHsh4okkvi1dCiNHwv33Q6lSvtNESxbQxDm3w8xSgZlm9olzbpbvYD48Pf1pZqyYQcaqDCqXqswn\nnT+hVJHQ/tsntCMWdOfcdDOrFv0okhBeeAFSU6FnT99JosY554AduVdTc7+Sbl+DA1vHZe3NYtnm\nZVT/d3W1jotTBZlDv9PMvs6dkil9qDuZWXczm2tmc9evX1+Ap5O40KYN9O4Nx4e724yZFTKz+cA6\nYJJzLuMg9wn1sb3srmV0OKsDKbllIq1QmlrHxbn8FvT+wMlAbSAT+Neh7uicS3fO1XPO1Stfvnw+\nn07iRvPmcM89vlNEnXNur3OuNlAFqG9mf+p6HfZju1LJSixYt4AcckhNSWVPzh61jotz+Srozrm1\nuQd8DvA6UD+ysSTubN0Kjz4K69b5ThJTzrktwFTgCt9ZYu29Be+xYP0Czq14LnNumUOPuj1Ys2ON\n71hyGPk6scjMKjnn9jUFvBpYcLj7SwgMGAD/+Ecw5VKhgu80UWVm5YFfnXNbzKwo0BTo4zlWTC1a\nv4ibx9xMwxMb8t+//ZfUQqlqHZcAjljQzWwY0AgoZ2argMeBRmZWm+CDouXArVHMKL7t3g0vvRTs\neV63ru80sVAJGGRmhQj+in3fOfeR50wxs2PPDq59/1qKH1Oc4dcOJ7VQqu9Ikkd5WeXS8SA3vxmF\nLBKv3nknaC/37ru+k8SEc+5roI7vHD4457hl7C0s2biEyTdM5oSSJ/iOJEdBe7nI4e3dC88/H4zM\nmzTxnUairN+cfry34D2ebfIsjas39h1HjpIKuhze1q1BN6IOHdSRKORmrZpFrwm9aHlaSx5o+IDv\nOJIPKuhyeGXKwIgRvlNIlK3fuZ52I9pRpVQVBrcZTIppm6dEpIIuh/bNN1C4MJx5pu8kEkV7c/bS\neVRn1u9cz+fdPqd00UOeJyhxTgVdDq1XL1i0CFasCAq7hNJTnz7FpGWTSG+Zrn6gCU5/V8nBzZsH\nkycHe7aomIfW+O/H8/T0p7mx9o3cfN7NvuNIAamgy8H16RPspnirTjEIqxVbVtB5VGfOrni2GlWE\nhAq6/Nm+LXJvvz3Y91xCJys7i3Yj2pGdk83I60ZSLLWY70gSAfpbWv5s3rygkId4i9xk12tCL+b8\nPIdR143i1DKn+o4jEaIRuvxZhw6wenXot8hNVkO+HsJrc1/jvgvv4+ozr/YdRyJIBV3+6KefwDko\nWtR3EomChesW0v2j7lx84sX0vry37zgSYSro8rutW+Hss+Hvf/edRCIsc3smDQc25Kr3rqLkMSUZ\nfu1wCqdoxjVs9C8qvxswALZtg2uu8Z1EIuypT5/is5WfATC1y1QqlazkOZFEgwq6BJJvi9ykcGBf\nUIDGgxqTVjhNfUFDSFMuEti3Re6DD/pOIhG07K5ltD699W/XixUupr6gIaaCLoFBg7RFbggdX+J4\nvsr8CoAihYqwe+9u9QUNMU25SGDSpGCpos4WDJXhC4ezcttKGlRtQL8W/Uifl07mjswj/6IkJBX0\nZJeVFRTxokXhVJ1gEiabd22m5/ie1DuhHp/e+CmFUgqpL2jIacol2b38crA97qZNvpNIhD0w+QE2\n/rKR9JbpFEop5DuOxIAKejLbvBmefRbOOCNoZCGhMWPFDF7/8nXuueAe6lRKyvaoSUkFPZk991xw\nMlFvnTEYJlnZWdz60a2cdOxJPNHoCd9xJIY0h56sVq6Ef/8bbrgBzjnHdxqJoD6f9WHxhsWM6zSO\n4scU9x1HYkgj9GT15pvBni1PPeU7iUTQkg1LeGbGM3So1YHmNZr7jiMxpoKerB57DDIy4KSTfCeR\nCHHO0ePjHhRLLUbfv/b1HUc80JRLMtqxA0qUgNq1fSeRCHp7/ttMWz6N9JbpVCxR0Xcc8UAj9GTz\n2WdQpQp8/rnvJBJB63au475J99HwxIZ0O6+b7zjiiQp6MnEOHngAihWDc8/1nUYiqNeEXmzP2k56\ny3RSTG/rZKUpl2QydmwwQh8wAIpr9UNYTPphEkO+GcJjlzzGmeXP9B1HPNL/ypNFdjY89BCcdhrc\ndJPvNBIhv/z6Cz0+7sFpZU/joYsf8h1HPNMIPVlMnQqLFsEHH0Bh/bOHxdOfPs2yzcuY2mUqaYXT\nfMcRz444QjezgWa2zswW7HdbGTObZGZLcy9LRzemFFjTpvDll+pGlAdmVtXMpprZIjNbaGY9fWc6\nmK/Xfs0LX7xA19pdaVStke84EgfyMuXyNnDFAbc9CExxztUApuRel3i1Y0dwWaeOtsfNm2zgXudc\nTeAC4A4zq+k50x+s2rqKhgMbUqpIKf7Z9J++40icOGJBd85NBw7ciu8qYFDu94OANhHOJZGyeTOc\nfDL07+87ScJwzmU6577M/X47sBio7DfVH3Uc2ZHte7ZT5/g6lC1W1ncciRP5nUyt6Jzbt0v+GkBn\nMcSr556DDRugQQPfSRKSmVUD6gAZfpMEDuwROuXHKdiTph6hAkRglYtzzgHuUD83s+5mNtfM5q5f\nv76gTydHQxtwFYiZlQBGAnc757Yd5OcxP7aX3bWMM8v9vjRRPUJlf/kt6GvNrBJA7uW6Q93ROZfu\nnKvnnKtXvnz5fD6d5MsTT2gDrnwys1SCYj7EOTfqYPfxcWynFkpl6calAKQVTlOPUPmD/Bb0MUCX\n3O+7AKMjE0ciZsMGGDYM7rhDG3AdJTMz4E1gsXPuRd959td3Vl+yXTbtz2rPrG6z6FG3B2t2rPEd\nS+LEEefQzWwY0AgoZ2argMeB54D3zawbsAK4LpohJR/KlQvWnZcq5TtJImoA3AB8Y2bzc2972Dk3\nzmMmNu/azMsZL3NtzWt579r3ANQjVP7giAXdOdfxED+6LMJZJFL27aZYrZrvJAnJOTcTiLv1nS9n\nvMz2Pdv5+8V/9x1F4pRO/Q8b5+CKK+DGG30nkQjalrWNvhl9uer0qzj3eG2sJgengh42+zbguvBC\n30kkgl6d/Spbdm/h0Use9R1F4pgKephkZQXb42oDrlDZsWcHL37xIi1qtKDuCXV9x5E4pl2awqRP\nH/j2W/j4Y0hN9Z1GIqT/nP5s3LVRo3M5Io3QwyIrK2j83KEDtGjhO41EyC+//sILX7xA05ObckGV\nC3zHkTinEXpYFCkC8+fD3r2+k0gEpc9LZ93OdRqdS55ohB4G8+fDr79C6dLB+nMJhd3Zu3n+s+dp\nVK0RF590se84kgBU0BPdzz/DpZdCz7jcslsK4M0v3yRzR6ZG55JnKuiJ7q67YM8e6NXLdxKJoKzs\nLJ777DkaVG1A42qNfceRBKE59EQ2ejSMHAnPPgunnuo7jUTQoP8bxKptq3ij1RuYmpJIHmmEnqi2\nbQs23jrnHLjvPt9pJIJ+3fsrvWf2pn7l+jQ7pZnvOJJANEJPVGvXBh+ApqdrzXnIDPlmCMu3LOeV\n5q9odC5HRQU9UdWoETR9TtEfWWGycutKbvv4NmqVr8WVNa70HUcSjKpBotmzB558ErZuVTEPoa6j\nu7I7ezeVS1XW6FyOmkboiaZPn6ATUf360Ly57zQSIQf2Cp3wwwT1CpWjpiFeIvn2W/jHP6B9exXz\nkFl21zJa1Ph9ywb1CpX8UEFPFDk50L07FC8eNH6WUKlUshKZ2zMBKFKoiHqFSr5oyiVRvPEGzJgR\nbMBVsaLvNBJhzjmWbFhC1VJVGdtxLOnz0snckek7liQYFfRE0bQpPPQQdO3qO4lEQcbqDH7J/oXX\nGr/Gucefq16hki8q6InAOahePTgjVEJp6DdDKVKoCFefebXvKJLANIce70aPhpYtYeNG30kkSrJz\nsnl/4fu0Or0VpYqU8h1HEphG6PFs3+n9ZctCKb3Rw2rqj1NZu3MtHWt19B1FEpwKejx76KFge9xR\no3R6f4gNWzCMUkVK/WHZokh+aMolXn3+OfTvH2yPW7++7zQSJbuzdzNy8UiuOfMa0gqn+Y4jCU4F\nPV49/jhUqRKcSCShNW7pOLZlbaNTrU6+o0gIaMolXr33HqxcCSVK+E4iUTRswTAqFq9I4+pqYiEF\npxF6vJk0KdiAq2xZqF3bdxqJom1Z2xi7ZCzXnXUdhVM0tpKCU0GPJ1OmwBVXQO/evpNIDPzvt/9L\n1t4sOp2t6RaJDBX0eLFyJXToAGecAffe6zuNxMDQb4ZS/bjqnF/5fN9RJCRU0ONBVha0awe7dwc9\nQjVvHnrrdq5j8rLJdKzVUfueS8Ro4i4ePPggZGTAiBHBCF1Cb8TCEex1ezXdIhFVoIJuZsuB7cBe\nINs5Vy8SoZJOt27BEsVrr/WdRAAzGwi0BNY552pF+vEzt2fy8H8f5oyyZ3BWhbMi/fCSxCIx5dLY\nOVdbxTwfNm0KLmvV0rx5fHkbuCJaD37/pPvZlrWNY9OOjdZTSJLSlIsvW7bA+edD27bw3HO+08h+\nnHPTzaxapB/3wDZzGasz1GZOIqqgI3QHTDazeWbW/WB3MLPuZjbXzOauX7++gE8XEjk5cOONsHw5\ntGrlO43k09Ee28vuWkanWp1IyX3bqc2cRFpBC3pD51xtoDlwh5ldcuAdnHPpzrl6zrl65cuXL+DT\nhcTzzwfb4r7wAjRo4DuN5NPRHtuVSlaixDElyCGHQlZIbeYk4go05eKcW517uc7MPgTqA9MjESy0\npkyBRx4JGj3fdZfvNBJjSzctBeDZJs+yYusKtZmTiMp3QTez4kCKc2577vfNgKciliysdu0Kdk98\n4w3Q+uOk0/6s9kxdPpV2Z7WjeunqvuNIyBRkyqUiMNPM/g+YDXzsnBsfmVgh1rJlsDWuTh6KW2Y2\nDPgCON3MVplZt0g99uzVsylXrBzVjqsWqYcU+U2+R+jOuWXAuRHMEm69ekG1asE0i0bmcc05F7XW\nQbN/nk39yvV1dqhEhU79j4UhQ+Cll+Cnn3wnEY927NnBovWL+MsJf/EdRUJKBT3avv4abrkFLrlE\n682T3JeZX5LjcqhfWR2oJDpU0KNp8WJo1gxKl4bhw6GwzuNKZrNXzwbQCF2iRgU9mr74AlJSgqWK\nx2utcbKbvXo21Y+rTvniOh9DokMFPRp+/TW4vOkm+PZb7aAoQFDQ/1JZo3OJHhX0SFu0KCjg03PP\nrypVym8eiQvrdq5jxdYV1D9B8+cSPZrUjaRFi6BJk2BZYoUKvtNIHJmzeg6APhCVqNIIPVL2L+ZT\np2qaRf5g9urZpFgK51U6z3cUCTEV9Ej46ScVczmsOT/P4azyZ1H8mOK+o0iIqaBHQuXKQYNnFXM5\nCOccs1fP1nSLRJ3m0Ati8eLgQ8/KlaFvX99pJE79uOVHNu7aqIIuUacRen4tWgSNG0PHjuCc7zQS\nxyZ8PwGA6sdpd0WJLhX0/Nj3AShAero225LD+s/c/wAwcvFIz0kk7DTlcrT2L+bTpmnOXA7pwB6i\nA+YNYMC8AeohKlGjEfrRuuee4FLFXI5gXw/RfdRDVKJNI/S8ci6YWhkyBDZuhNNP951I4lylkpUo\nWaQkAIWtsHqIStSpoB9JTk7QzHnaNPjoIyhXLvgSyYOft/8MwJ3n30lWdpZ6iEpUacrlcFatgssv\nhwcegGLFgn6gIkfhleavAFCrQi36XdmPUe1HeU4kYaaCfiijRsE558Ds2fDmmzBiBBTXWX5ydDbv\n3gxAmaLdp6zeAAAF/klEQVRlPCeRZKApl4PZtSv48POUU2DoUKhRw3ciSVCbdm0CVNAlNlTQ9/f1\n18GHnUWLBk0pTjwRjjnGdypJYPsKeum00p6TSDLQlAsEH3w+/zzUq/d7389TT1UxlwLTCF1iSSP0\nVavgb38LNtZq2xbuvNN3IgkRFXSJpeQu6BMnBrskZmXBG28ELeN0Gr9E0KZdm0grnEbR1KK+o0gS\nSO6CfsIJUKtWUMxPO813GgmhTbs2aXQuMZNcBT0rC4YPh+++g3/8Iyjmn36qUblEjQq6xFJyfCj6\n88/w2GPBqpUuXYI15uvXBz9TMZcoUkGXWAp/QR87Fk46KRiRn39+MG++cCGUL+87mSQBFXSJpfAV\n9KwsGDwYxo8PrjdoAHfdBUuXwpgx0LSpRuUSM5t2baJMmgq6xEZ4CvqB0yqDBgW3lykD//pXcNan\nSIxphC6xVKCCbmZXmNkSM/vezB6MVKij9uijf55WGTrUWxxJfJE4tnf9uotd2btU0CVm8r3KxcwK\nAf2ApsAqYI6ZjXHOLYpUuN84F+xB/v33wdTJvstnnoHq1YPT9e+8E+64QyNxKbBIHdvfbvgWgEJW\nKOIZRQ6mIMsW6wPfO+eWAZjZe8BVQP4KunOwaVNQqPcV7bZtgx0PP/4YWrX6/b4pKcGIfM2aoKBf\nf33wJRIZETm2n/ss2EZiyo9TuL/h/ZHOKPInBSnolYGV+11fBZyfr0f69lu48ELYsuX321JS4OST\ng4Jepw68+GKw6+GppwZFvEiRAkQXOawCHdsH9hKduGwi9qSpl6hEXdRPLDKz7kB3gBNPPPHgd6pc\nGTp2DAr2wYp25cq/9/IUiROHOraX3bWM+ybex6jFo9i9dzdphdNoe2ZbXmj2gq+okiQKUtBXA1X3\nu14l97Y/cM6lA+kA9erVcwd9pJIl4bXXChBFJKIKdGxXKlmJUkVKsSdnD2mF09izd496iUpMFGSV\nyxyghplVN7NjgA7AmMjEEvGqwMf22p1r6VG3B7O6zaJH3R6s2bEmKkFF9pfvEbpzLtvM/h8wASgE\nDHTOLYxYMhFPInFs7987tN+V/SIbUOQQCjSH7pwbB4yLUBaRuKFjWxJReM4UFRFJciroIiIhoYIu\nIhISKugiIiGhgi4iEhLm3MHP9YnKk5mtB1Yc4sflgA0xCxNbYX5tED+v7yTnnJfOJYc5tuPlv020\nhPn1xdNry9OxHdOCfjhmNtc5V893jmgI82uD8L++ggj7f5swv75EfG2achERCQkVdBGRkIingp7u\nO0AUhfm1QfhfX0GE/b9NmF9fwr22uJlDFxGRgomnEbqIiBSA94IeN42mo8DMqprZVDNbZGYLzayn\n70yRZmaFzOwrM/vId5Z4E7Zj28wGmtk6M1uw321lzGySmS3NvSztM2N+Heq9mmivz2tB368Zb3Og\nJtDRzGr6zBRh2cC9zrmawAXAHSF7fQA9gcW+Q8SbkB7bbwNXHHDbg8AU51wNYEru9UR0qPdqQr0+\n3yP035rxOuf2APua8YaCcy7TOfdl7vfbCQpfZb+pIsfMqgBXAm/4zhKHQndsO+emA5sOuPkqYFDu\n94OANjENFSGHea8m1OvzXdAP1ow3NAVvf2ZWDagDZPhNElF9gfuBHN9B4lCyHNsVnXOZud+vASr6\nDBMJB7xXE+r1+S7oScHMSgAjgbudc9t854kEM2sJrHPOzfOdReKDC5bMJfSyucO9VxPh9fku6Hlq\nxpvIzCyV4AAZ4pwbdaT7J5AGQGszW04wndDEzN71GymuhP7YzrXWzCoB5F6u85wn3w7xXk2o1+e7\noIe60bSZGfAmsNg596LvPJHknHvIOVfFOVeN4N/tv8656z3HiiehPrb3Mwbokvt9F2C0xyz5dpj3\nakK9Pq8F3TmXDexrxrsYeD9kjaYbADcQjF7n53618B1Koi+Mx7aZDQO+AE43s1Vm1g14DmhqZkuB\ny3OvJ6JDvVcT6vXpTFERkZDwPeUiIiIRooIuIhISKugiIiGhgi4iEhIq6CIiIaGCLiISEiroIiIh\noYIuIhIS/x9Kk1poUnUOtQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b3f6b7ffd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plt.subplot(nrows, ncols, plot_number)\n",
    "plt.subplot(1,2,1)\n",
    "plt.plot(x, y, 'r--') # More on color options later\n",
    "plt.subplot(1,2,2)\n",
    "plt.plot(y, x, 'g*-');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___\n",
    "# Matplotlib Object Oriented Method\n",
    "Now that we've seen the basics, let's break it all down with a more formal introduction of Matplotlib's Object Oriented API. This means we will instantiate figure objects and then call methods or attributes from that object."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction to the Object Oriented Method"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The main idea in using the more formal Object Oriented method is to create figure objects and then just call methods or attributes off of that object. This approach is nicer when dealing with a canvas that has multiple plots on it. \n",
    "\n",
    "To begin we create a figure instance. Then we can add axes to that figure:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x2b3f6d44a58>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAY0AAAEjCAYAAADOsV1PAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nMDOzzBwaZmaWmUPDzMwyc2iYmVlm/wNZSUGQ0B+MzgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b3f6ceba90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create Figure (empty canvas)\n",
    "fig = plt.figure()\n",
    "\n",
    "# Add set of axes to figure\n",
    "axes = fig.add_axes([0.1, 0.1, 0.8, 0.8]) # left, bottom, width, height (range 0 to 1)\n",
    "\n",
    "# Plot on that set of axes\n",
    "axes.plot(x, y, 'b')\n",
    "axes.set_xlabel('Set X Label') # Notice the use of set_ to begin methods\n",
    "axes.set_ylabel('Set y Label')\n",
    "axes.set_title('Set Title')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Code is a little more complicated, but the advantage is that we now have full control of where the plot axes are placed, and we can easily add more than one axis to the figure:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAY4AAAEkCAYAAAA4g9b0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nMcbkiAWHMcaYHLHgMMYYkyP/D0eyrd0IaGuXAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b3f6d8c978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Creates blank canvas\n",
    "fig = plt.figure()\n",
    "\n",
    "axes1 = fig.add_axes([0.1, 0.1, 0.8, 0.8]) # main axes\n",
    "axes2 = fig.add_axes([0.2, 0.5, 0.4, 0.3]) # inset axes\n",
    "\n",
    "# Larger Figure Axes 1\n",
    "axes1.plot(x, y, 'b')\n",
    "axes1.set_xlabel('X_label_axes2')\n",
    "axes1.set_ylabel('Y_label_axes2')\n",
    "axes1.set_title('Axes 2 Title')\n",
    "\n",
    "# Insert Figure Axes 2\n",
    "axes2.plot(y, x, 'r')\n",
    "axes2.set_xlabel('X_label_axes2')\n",
    "axes2.set_ylabel('Y_label_axes2')\n",
    "axes2.set_title('Axes 2 Title');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## subplots()\n",
    "\n",
    "The plt.subplots() object will act as a more automatic axis manager.\n",
    "\n",
    "Basic use cases:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAEWCAYAAABrDZDcAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nUk5BICKScgoCEZGU+z9UcDxchBP53wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b3f6d93c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Use similar to plt.figure() except use tuple unpacking to grab fig and axes\n",
    "fig, axes = plt.subplots()\n",
    "\n",
    "# Now use the axes object to add stuff to plot\n",
    "axes.plot(x, y, 'r')\n",
    "axes.set_xlabel('x')\n",
    "axes.set_ylabel('y')\n",
    "axes.set_title('title');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then you can specify the number of rows and columns when creating the subplots() object:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXwAAAD8CAYAAAB0IB+mAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b3f677f0b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Empty canvas of 1 by 2 subplots\n",
    "fig, axes = plt.subplots(nrows=1, ncols=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<matplotlib.axes._subplots.AxesSubplot object at 0x000002B3F6F41A58>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x000002B3F6FABCF8>], dtype=object)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Axes is an array of axes to plot on\n",
    "axes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can iterate through this array:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAEWCAYAAABrDZDcAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b3f677f0b8>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for ax in axes:\n",
    "    ax.plot(x, y, 'b')\n",
    "    ax.set_xlabel('x')\n",
    "    ax.set_ylabel('y')\n",
    "    ax.set_title('title')\n",
    "\n",
    "# Display the figure object    \n",
    "fig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A common issue with matplolib is overlapping subplots or figures. We ca use **fig.tight_layout()** or **plt.tight_layout()** method, which automatically adjusts the positions of the axes on the figure canvas so that there is no overlapping content:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAagAAAEYCAYAAAAJeGK1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b3f70666a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(nrows=1, ncols=2)\n",
    "\n",
    "for ax in axes:\n",
    "    ax.plot(x, y, 'g')\n",
    "    ax.set_xlabel('x')\n",
    "    ax.set_ylabel('y')\n",
    "    ax.set_title('title')\n",
    "\n",
    "fig    \n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Figure size, aspect ratio and DPI"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Matplotlib allows the aspect ratio, DPI and figure size to be specified when the Figure object is created. You can use the `figsize` and `dpi` keyword arguments. \n",
    "* `figsize` is a tuple of the width and height of the figure in inches\n",
    "* `dpi` is the dots-per-inch (pixel per inch). \n",
    "\n",
    "For example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b3f6da0c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(8,4), dpi=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The same arguments can also be passed to layout managers, such as the `subplots` function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtEAAADgCAYAAADFcDomAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b3f6f58320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(figsize=(12,3))\n",
    "\n",
    "axes.plot(x, y, 'r')\n",
    "axes.set_xlabel('x')\n",
    "axes.set_ylabel('y')\n",
    "axes.set_title('title');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Saving figures\n",
    "Matplotlib can generate high-quality output in a number formats, including PNG, JPG, EPS, SVG, PGF and PDF. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To save a figure to a file we can use the `savefig` method in the `Figure` class:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fig.savefig(\"filename.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we can also optionally specify the DPI and choose between different output formats:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fig.savefig(\"filename.png\", dpi=200)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "____\n",
    "## Legends, labels and titles"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have covered the basics of how to create a figure canvas and add axes instances to the canvas, let's look at how decorate a figure with titles, axis labels, and legends."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Figure titles**\n",
    "\n",
    "A title can be added to each axis instance in a figure. To set the title, use the `set_title` method in the axes instance:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ax.set_title(\"title\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Axis labels**\n",
    "\n",
    "Similarly, with the methods `set_xlabel` and `set_ylabel`, we can set the labels of the X and Y axes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ax.set_xlabel(\"x\")\n",
    "ax.set_ylabel(\"y\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Legends"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can use the **label=\"label text\"** keyword argument when plots or other objects are added to the figure, and then using the **legend** method without arguments to add the legend to the figure: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2b3f6ed2c18>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAdwAAAFCCAYAAABbz2zGAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b3f6cfaa20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "\n",
    "ax = fig.add_axes([0,0,1,1])\n",
    "\n",
    "ax.plot(x, x**2, label=\"x**2\")\n",
    "ax.plot(x, x**3, label=\"x**3\")\n",
    "ax.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice how are legend overlaps some of the actual plot!\n",
    "\n",
    "The **legend** function takes an optional keyword argument **loc** that can be used to specify where in the figure the legend is to be drawn. The allowed values of **loc** are numerical codes for the various places the legend can be drawn. See the [documentation page](http://matplotlib.org/users/legend_guide.html#legend-location) for details. Some of the most common **loc** values are:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAdwAAAFCCAYAAABbz2zGAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b3f6cfaa20>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Lots of options....\n",
    "\n",
    "ax.legend(loc=1) # upper right corner\n",
    "ax.legend(loc=2) # upper left corner\n",
    "ax.legend(loc=3) # lower left corner\n",
    "ax.legend(loc=4) # lower right corner\n",
    "\n",
    "# .. many more options are available\n",
    "\n",
    "# Most common to choose\n",
    "ax.legend(loc=0) # let matplotlib decide the optimal location\n",
    "fig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setting colors, linewidths, linetypes\n",
    "\n",
    "Matplotlib gives you *a lot* of options for customizing colors, linewidths, and linetypes. \n",
    "\n",
    "There is the basic MATLAB like syntax (which I would suggest you avoid using for more clairty sake:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Colors with MatLab like syntax"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With matplotlib, we can define the colors of lines and other graphical elements in a number of ways. First of all, we can use the MATLAB-like syntax where `'b'` means blue, `'g'` means green, etc. The MATLAB API for selecting line styles are also supported: where, for example, 'b.-' means a blue line with dots:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2b3f76d6e10>]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAD8CAYAAAB5Pm/hAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nTkUvIhLl/h9Dyuc3sxNDWwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b3f70784e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# MATLAB style line color and style \n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(x, x**2, 'b.-') # blue line with dots\n",
    "ax.plot(x, x**3, 'g--') # green dashed line"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Colors with the color= parameter"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also define colors by their names or RGB hex codes and optionally provide an alpha value using the `color` and `alpha` keyword arguments. Alpha indicates opacity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2b3f774efd0>]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAW4AAAD8CAYAAABXe05zAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b3f6c6f2e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "ax.plot(x, x+1, color=\"blue\", alpha=0.5) # half-transparant\n",
    "ax.plot(x, x+2, color=\"#8B008B\")        # RGB hex code\n",
    "ax.plot(x, x+3, color=\"#FF8C00\")        # RGB hex code "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Line and marker styles"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To change the line width, we can use the `linewidth` or `lw` keyword argument. The line style can be selected using the `linestyle` or `ls` keyword arguments:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAswAAAFpCAYAAACI6H7aAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAAqIDADAAAAFRAYAYAAAAq+B8SRuWGI+PtEgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b3f7700400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12,6))\n",
    "\n",
    "ax.plot(x, x+1, color=\"red\", linewidth=0.25)\n",
    "ax.plot(x, x+2, color=\"red\", linewidth=0.50)\n",
    "ax.plot(x, x+3, color=\"red\", linewidth=1.00)\n",
    "ax.plot(x, x+4, color=\"red\", linewidth=2.00)\n",
    "\n",
    "# possible linestype options ‘-‘, ‘–’, ‘-.’, ‘:’, ‘steps’\n",
    "ax.plot(x, x+5, color=\"green\", lw=3, linestyle='-')\n",
    "ax.plot(x, x+6, color=\"green\", lw=3, ls='-.')\n",
    "ax.plot(x, x+7, color=\"green\", lw=3, ls=':')\n",
    "\n",
    "# custom dash\n",
    "line, = ax.plot(x, x+8, color=\"black\", lw=1.50)\n",
    "line.set_dashes([5, 10, 15, 10]) # format: line length, space length, ...\n",
    "\n",
    "# possible marker symbols: marker = '+', 'o', '*', 's', ',', '.', '1', '2', '3', '4', ...\n",
    "ax.plot(x, x+ 9, color=\"blue\", lw=3, ls='-', marker='+')\n",
    "ax.plot(x, x+10, color=\"blue\", lw=3, ls='--', marker='o')\n",
    "ax.plot(x, x+11, color=\"blue\", lw=3, ls='-', marker='s')\n",
    "ax.plot(x, x+12, color=\"blue\", lw=3, ls='--', marker='1')\n",
    "\n",
    "# marker size and color\n",
    "ax.plot(x, x+13, color=\"purple\", lw=1, ls='-', marker='o', markersize=2)\n",
    "ax.plot(x, x+14, color=\"purple\", lw=1, ls='-', marker='o', markersize=4)\n",
    "ax.plot(x, x+15, color=\"purple\", lw=1, ls='-', marker='o', markersize=8, markerfacecolor=\"red\")\n",
    "ax.plot(x, x+16, color=\"purple\", lw=1, ls='-', marker='s', markersize=8, \n",
    "        markerfacecolor=\"yellow\", markeredgewidth=3, markeredgecolor=\"green\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Control over axis appearance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this section we will look at controlling axis sizing properties in a matplotlib figure."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot range"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can configure the ranges of the axes using the `set_ylim` and `set_xlim` methods in the axis object, or `axis('tight')` for automatically getting \"tightly fitted\" axes ranges:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAs4AAAEICAYAAABPtXIYAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nJEGFs4iIiIhIEv4f8szxbTyungQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b3f7774ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 3, figsize=(12, 4))\n",
    "\n",
    "axes[0].plot(x, x**2, x, x**3)\n",
    "axes[0].set_title(\"default axes ranges\")\n",
    "\n",
    "axes[1].plot(x, x**2, x, x**3)\n",
    "axes[1].axis('tight')\n",
    "axes[1].set_title(\"tight axes\")\n",
    "\n",
    "axes[2].plot(x, x**2, x, x**3)\n",
    "axes[2].set_ylim([0, 60])\n",
    "axes[2].set_xlim([2, 5])\n",
    "axes[2].set_title(\"custom axes range\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Special Plot Types\n",
    "\n",
    "There are many specialized plots we can create, such as barplots, histograms, scatter plots, and much more. Most of these type of plots we will actually create using pandas. But here are a few examples of these type of plots:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x2b3f792af98>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD8CAYAAABn919SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nOgAkgkAHgEQQ6ACQCAIdABLx/+2jwypeg70nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b3f6c93f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 15.,   6.,  12.,   9.,   7.,  13.,   6.,  16.,  10.,   6.]),\n",
       " array([   2. ,  101.6,  201.2,  300.8,  400.4,  500. ,  599.6,  699.2,\n",
       "         798.8,  898.4,  998. ]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD8CAYAAABn919SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b3f3c79400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from random import sample\n",
    "data = sample(range(1, 1000), 100)\n",
    "plt.hist(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXYAAAD8CAYAAABjAo9vAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ngGT+H0S3VUr22AqiAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b3f78511d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = [np.random.normal(0, std, 100) for std in range(1, 4)]\n",
    "\n",
    "# rectangular box plot\n",
    "plt.boxplot(data,vert=True,patch_artist=True);   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Further reading"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* http://www.matplotlib.org - The project web page for matplotlib.\n",
    "* https://github.com/matplotlib/matplotlib - The source code for matplotlib.\n",
    "* http://matplotlib.org/gallery.html - A large gallery showcaseing various types of plots matplotlib can create. Highly recommended! \n",
    "* http://www.loria.fr/~rougier/teaching/matplotlib - A good matplotlib tutorial.\n",
    "* http://scipy-lectures.github.io/matplotlib/matplotlib.html - Another good matplotlib reference.\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}